comparison toolboxes/FullBNT-1.0.7/netlab3.3/mlphdotv.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 function hdv = mlphdotv(net, x, t, v)
2 %MLPHDOTV Evaluate the product of the data Hessian with a vector.
3 %
4 % Description
5 %
6 % HDV = MLPHDOTV(NET, X, T, V) takes an MLP network data structure NET,
7 % together with the matrix X of input vectors, the matrix T of target
8 % vectors and an arbitrary row vector V whose length equals the number
9 % of parameters in the network, and returns the product of the data-
10 % dependent contribution to the Hessian matrix with V. The
11 % implementation is based on the R-propagation algorithm of
12 % Pearlmutter.
13 %
14 % See also
15 % MLP, MLPHESS, HESSCHEK
16 %
17
18 % Copyright (c) Ian T Nabney (1996-2001)
19
20 % Check arguments for consistency
21 errstring = consist(net, 'mlp', x, t);
22 if ~isempty(errstring);
23 error(errstring);
24 end
25
26 ndata = size(x, 1);
27
28 [y, z] = mlpfwd(net, x); % Standard forward propagation.
29 zprime = (1 - z.*z); % Hidden unit first derivatives.
30 zpprime = -2.0*z.*zprime; % Hidden unit second derivatives.
31
32 vnet = mlpunpak(net, v); % Unpack the v vector.
33
34 % Do the R-forward propagation.
35
36 ra1 = x*vnet.w1 + ones(ndata, 1)*vnet.b1;
37 rz = zprime.*ra1;
38 ra2 = rz*net.w2 + z*vnet.w2 + ones(ndata, 1)*vnet.b2;
39
40 switch net.outfn
41
42 case 'linear' % Linear outputs
43
44 ry = ra2;
45
46 case 'logistic' % Logistic outputs
47
48 ry = y.*(1 - y).*ra2;
49
50 case 'softmax' % Softmax outputs
51
52 nout = size(t, 2);
53 ry = y.*ra2 - y.*(sum(y.*ra2, 2)*ones(1, nout));
54
55 otherwise
56 error(['Unknown activation function ', net.outfn]);
57 end
58
59 % Evaluate delta for the output units.
60
61 delout = y - t;
62
63 % Do the standard backpropagation.
64
65 delhid = zprime.*(delout*net.w2');
66
67 % Now do the R-backpropagation.
68
69 rdelhid = zpprime.*ra1.*(delout*net.w2') + zprime.*(delout*vnet.w2') + ...
70 zprime.*(ry*net.w2');
71
72 % Finally, evaluate the components of hdv and then merge into long vector.
73
74 hw1 = x'*rdelhid;
75 hb1 = sum(rdelhid, 1);
76 hw2 = z'*ry + rz'*delout;
77 hb2 = sum(ry, 1);
78
79 hdv = [hw1(:)', hb1, hw2(:)', hb2];